Image de couverture de livre

Exploring the Expertise of Large Language Models in Materials Science and Metallurgical Engineering

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Bajan, Christophe, Lambard, Guillaume
Format:	Preprint
Publié:	2025
Sujets:	Computational Physics Materials Science
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Beyond Structure: Revolutionising Materials Discovery via AI-Driven Synthesis Protocol-Property Relationships
par: Lambard, Guillaume
Publié: (2026)

Engineering Point Defects in MoS2 for Tailored Material Properties using Large Language Models
par: Al-Maeeni, Abdalaziz, et autres
Publié: (2025)

Deep Potentials for Materials Science
par: Wen, Tongqi, et autres
Publié: (2022)

Mining experimental data from Materials Science literature with Large Language Models: an evaluation study
par: Foppiano, Luca, et autres
Publié: (2024)

Large Language Models for Material Property Predictions: elastic constant tensor prediction and materials design
par: Liu, Siyu, et autres
Publié: (2024)

Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie
par: Peivaste, Iman, et autres
Publié: (2026)

MaterialBENCH: Evaluating College-Level Materials Science Problem-Solving Abilities of Large Language Models
par: Yoshitake, Michiko, et autres
Publié: (2024)

MatTools: Benchmarking Large Language Models for Materials Science Tools
par: Liu, Siyu, et autres
Publié: (2025)

A Python-Based Approach to Sputter Deposition Simulations in Combinatorial Materials Science
par: Thelen, Felix, et autres
Publié: (2024)

Building a Regional Data-Centric Materials Science Ecosystem for Processing-Rich Materials Innovation in the Great Plains
par: Mei, D. -M., et autres
Publié: (2026)

From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Research
par: Huang, Haonan
Publié: (2026)

Project For Advancement of Software Usability in Materials Science
par: Yoshimi, Kazuyoshi, et autres
Publié: (2025)

Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
par: Zimmermann, Yoel, et autres
Publié: (2024)

AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials
par: Hodapp, Max, et autres
Publié: (2026)

CrystalFlow: A Flow-Based Generative Model for Crystalline Materials
par: Luo, Xiaoshan, et autres
Publié: (2024)

Machine Learning-Driven Analytical Models for Threshold Displacement Energy Prediction in Materials
par: Duque, Rosty B. Martinez, et autres
Publié: (2025)

Band Structure Modeling of Perovskite Materials with Quantum ESPRESSO for Multijunction Photovoltaic Cell Optimization
par: Kalaiselvi, Sutharsika Kumar
Publié: (2025)

A Universal Spin-Orbit-Coupled Hamiltonian Model for Accelerated Quantum Material Discovery
par: Zhong, Yang, et autres
Publié: (2025)

Active Learning for Predicting the Enthalpy of Mixing inBinary Liquids Based on Ab Initio Molecular Dynamics
par: Bizot, Quentin, et autres
Publié: (2025)

Unconventional mechanical and thermal behaviors of MOF CALF-20
par: Fan, Dong, et autres
Publié: (2023)

Introduction to Engineering Materials
par: Arauzo, Ana
Publié: (2025)

Universal Numerical Simulation Model for Laser Material Processing
par: Otto, Andreas, et autres
Publié: (2025)

Investigating Material Interface Diffusion Phenomena through Graph Neural Networks in Applied Materials
par: Zhao, Zirui, et autres
Publié: (2024)

Enhanced Spin Hall Ratio in Two-Dimensional Semiconductors
par: Zhou, Jiaqi, et autres
Publié: (2023)

Guidelines for accurate and efficient calculations of mobilities in two-dimensional materials
par: Zhou, Jiaqi, et autres
Publié: (2024)

First-Principles Study of Transition Metal Doped in 2D Polyaramid for Novel Material Modelling
par: Trivedi, Ravi, et autres
Publié: (2025)

Integrated Experiment and Simulation Co-Design: A Key Infrastructure for Predictive Mesoscale Materials Modeling
par: Joshi, Shailendra P., et autres
Publié: (2025)

Constitutive Kolmogorov-Arnold Networks (CKANs): Combining Accuracy and Interpretability in Data-Driven Material Modeling
par: Abdolazizi, Kian P., et autres
Publié: (2025)

Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
par: Barroso-Luque, Luis, et autres
Publié: (2024)

LAMBench: A Benchmark for Large Atomistic Models
par: Peng, Anyang, et autres
Publié: (2025)

DiffCrysGen: A Score-Based Diffusion Model for Design of Diverse Inorganic Crystalline Materials
par: Mal, Sourav, et autres
Publié: (2025)

Synergizing Human Expertise and AI Efficiency with Language Model for Microscopy Operation and Automated Experiment Design
par: Liu, Yongtao, et autres
Publié: (2024)

Ions at electrochemical interfaces: from explicit to implicit molecular solvent descriptions
par: Nair, Swetha, et autres
Publié: (2025)

Beyond Independent Error Assumptions in Large GNN Atomistic Models
par: Ock, Janghoon, et autres
Publié: (2023)

Masgent: An AI-assisted Materials Simulation Agent
par: Liu, Guanghen, et autres
Publié: (2025)

Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering
par: Polak, Maciej P., et autres
Publié: (2023)

Foundational Large Language Models for Materials Research
par: Mishra, Vaibhav, et autres
Publié: (2024)

Mapping Microstructure: Manifold Construction for Accelerated Materials Exploration
par: Mason, Simon A., et autres
Publié: (2025)

A Foundational Potential Energy Surface Dataset for Materials
par: Kaplan, Aaron D., et autres
Publié: (2025)

The JARVIS Infrastructure is All You Need for Materials Design
par: Choudhary, Kamal
Publié: (2025)